Chapter 18. GIS MODELS AND MODELING 18.1 Basic Elements of GIS Modeling 18.1.1 Classification of GIS Models 18.1.2 The Modeling Process 18.1.3 The Role of GIS in Modeling
18.1.4 Integration of GIS and Other Modeling Programs 18.2 Binary Models
Box 18.1 The Conservation Reserve Program
18.3 Index Models
18.3.1 The Weighted Linear Combination Method
Box 18.2 Geoprocessing Tools in ArcGIS 9.0
18.3.2 Other Methods
18.3.3 Applications of the Index Model 18.4 Regression Models
18.4.1 Linear Regression Models 18.4.2 Logistic Regression Models 18.5 Process Models
18.5.1 Soil Erosion Models
Box 18.3 Explanation of the Six Factors in RUSLE
18.5.2 Other Process Models 18.5.3 GIS and Process Models Key Concepts and Terms Review Questions
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Applications: GIS Models and Modeling Task 1: Build a Vector-Based Binary Model Task 2: Build a Raster-Based Binary Model Task 3: Build a Vector-Based Index Model Task 4: Build a Raster-Based Index Model Challenge Question
Model
zA model is a simplified representation of a phenomenon or a system.
zModels using geographically referenced data are usually called “spatially explicit models.”
Classification of GIS Models
1. A model may be descriptive or prescriptive. 2. A model may be deterministic or stochastic. 3. A model may be static or dynamic.
The Modeling Process
1. The first step is to define the goals of the model. 2. The second step is to break down the model into
elements and to define the properties of each element and the interactions between the elements. A flowchart is a useful tool for linking the elements.
3. The third step is the implementation and calibration of the model.
4. The fourth step is to validate the model before it can be generally accepted.
The Role of GIS in Modeling
1. A GIS is a tool that can process, display, and integrate different data sources including maps, digital elevation models (DEMs), GPS (global positioning system) data, images, and tables.
2. A GIS can be used to build a vector-based or raster-based model.
3. A GIS has algorithms for conversion between vector and raster data.
Binary Models
zA binary model uses logical expressions to select spatial features from a composite feature layer or multiple rasters. The output of a binary model is in binary format: 1 (true) for spatial features that meet the selection criteria and 0 (false) for features that do not.
zSiting analysis is probably the most common application of the binary model.
Figure 18.1
To build a vector-based binary model, first overlay the layers so that their spatial features and attributes (Suit and Type) are combined. Then, use the query statement, Suit = 2 AND Type = 18, to select polygon 4 and save it to the output.
Figure 18.2 To build a raster-based binary model, use the query statement, [Raster 1] = 3 AND [Raster 2] = 3, to select three cells (shaded) and save them to the output raster.
Index Models
zAn index model calculates the index value for each unit area and produces a ranked map based on the index values. zAn index model is similar to a binary model in that both involve multicriteria evaluation and both depend on overlay operations for data processing. But an index model produces for each unit area an index value rather than a simple yes or no.
The Weighted Linear Combination
Method
zThe weighted linear combination method is a common method for computing the index value.
zThe method involves evaluation at three levels. First, the relative importance of each criterion, or factor, is evaluated against other criteria. Second, data for each criterion are standardized. Third, the index value is calculated for each unit area by summing the weighted criterion values and dividing the sum by the total of the weights.
Figure 18.3
Figure 18.4
Building a vector-based index model requires several steps. First, standardize the Suit and Type values of the input layers into a scale of 0.0 to 1.0. Second, overlay the layers. Third, assign a weight of 0.4 to the layer with Suit and a weight of 0.6 to the layer with Type. Finally, calculate the index value for each polygon in the output by summing the weighted criterion values. For example, Polygon 4 has an index value of 0.26 (0.5*0.4 + 0.1*0.6).
Figure 18.5
Build a raster-based index model requires the following steps. First, standardize the cell values of each input raster into a scale of 0.0 to 1.0. Second, multiply each input raster by its criterion weight. Finally, calculate the index values in the output raster by summing the
Applications of the Index Model
Index models are commonly used for suitability analysis and vulnerability analysis.
Regression Models
zA regression model relates a dependent variable to a number of independent (explanatory) variables in an equation, which can then be used for prediction or estimation.
zA regression model can use overlay operations in a GIS to combine variables needed for the analysis.
zThere are two types of regression model: linear regression and logistic regression.
Process Models
zA process model integrates existing knowledge about the environmental processes in the real world into a set of relationships and equations for quantifying the processes. zEnvironmental models are typically process models because they must deal with the interaction of many variables including physical variables such as climate, topography, vegetation, and soils as well as cultural variables such as land management.
California LESA model
http://www.consrv.ca.gov/DLRP/qh_lesa.htm WEPP http://topsoil.nserl.purdue.edu/nserlweb/weppmain/wepp.html SWAT / SSURGO http://waterhome.tamu.edu/NRCSdata/SWAT_SSURGO BASINS http://www.epa.gov/waterscience/BASINS/
Conservation Reserve Program (CRP)
